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A comprehensive analysis of weighting and multicriteria methods in the context of sustainable energy

This study presents a comprehensive and comparative analysis of weighting and multiple attribute decision-making (MADM) methods in the context of sustainable energy. As the selection problems of energy involve various conflicting attributes, MADM methods have been widely applied in addressing these...

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Detalles Bibliográficos
Autor principal: Şahin, M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7490576/
https://www.ncbi.nlm.nih.gov/pubmed/32952577
http://dx.doi.org/10.1007/s13762-020-02922-7
Descripción
Sumario:This study presents a comprehensive and comparative analysis of weighting and multiple attribute decision-making (MADM) methods in the context of sustainable energy. As the selection problems of energy involve various conflicting attributes, MADM methods have been widely applied in addressing these issues. In this study, six weighting and seven MADM methods that constitute a total of 42 models are implemented to evaluate different weighting and multicriteria decision-making methods and determine the most efficient and sustainable energy option. To determine the weights of economic, environmental, socioeconomic, and technical attributes, two subjective methods—the analytic hierarchy process and best–worst method—and four objective methods—the criteria importance through intercriteria correlation, Shannon's entropy, standard deviation, and mean weight—are used. Thus, both expert evaluations and data-based assessments are considered. Using each attribute weight provided by the six methods, the ranking of electricity generation options for Turkey is obtained through seven MADM methods: the elimination and choice expressing the reality method, the weighted sum method, the weighted product method, the organization, rangement et synthese de donnes relationnelles (ORESTE) method, the technique for order performance by similarity to the ideal solution, the preference ranking organization method for the enrichment of evaluations, and the multiple criteria optimization compromise solution. Rankings obtained from all models are integrated through the Borda, Copeland, and grade average methods. The results indicate that hydro is the optimal electricity generation option, followed by onshore wind, solar PV, geothermal, natural gas, and coal.